Parties and candidates not only want to be present in the media (coverage bias), or evaluated in a positive way (tonality bias). They also want the media agenda to be congruent with their own agenda to define the issue-based criteria on which they will be evaluated by voters. Thus, parties choose their issue agenda carefully, highlighting issues that they are perceived to be competent on, that they “own” and that are important to their voters. In that sense agenda bias refers to the extent to which political actors appear in the public domain in conjunction with the topics they wish to emphasize.

To allow for an operationalization of agenda bias, I use parties’ campaign communication as an approximation of the potential universe of news stories (D’Alessio & Allen, 2000; Eberl, 2017). I compare the policy issues addressed in campaign communication (i.e., the party agenda) with the policy issues the parties address in media coverage (i.e., the mediated party agenda).

To discover the latent topics in the corpus of press releases (1.942) and news articles (11.880), a structural topic modeling (STM) developed by Roberts (2016) is applied. The STM is an unsupervised machine learning approach that models topics as multinomial distributions of words and documents as multinomial distributions of topics, allowing to incorporate external variables that effect both, topical content and topical prevalence.

Structural Topic Model

Build Corpus

Select Model

STM assumes a fixed user-specified number of topics. There is not a “right” answer to the number of topics that are appropriate for a given corpus (Grimmer and Stewart 2013), but the function searchK uses a data-driven approach to selecting the number of topics. The function will perform several automated tests to help choose the number of topics including calculating the held out likelihood (Wallach et al. 2009) and performing a residual analysis (Taddy 2012).

Run Model

I included the document source as a control for the topical topical prevalence and the type (press release or news article) as a control for topical content. Thus, I assume that the distribution of topics depends on the source and the word distribution within each topic differes between party press releases and news articles. The number of topics is set to 55.

Results

Label topics

To explore the words associated with each topic we use the words with the highest probability in each topic. As we included the source type (press release or news paper) as a control for the topical content (the word distribution of each topic), we have two different labels for each topic.

Topic frequency

The expected proportion of the corpus that belongs to each topic is used to get an initial overview of the results. The figure below displays the topics ordered by their expected frequency across the corpus. The four most frequent words in each topic are used as a label for that topic.

Measure Agendas

For each document, we have a distribution over all topics:

What is the document acutally about?

Agendas were measured in terms of percentage distributions across the 55 topics. For each source the average distribution of each topic is calculated.

Then, we estimated bivariate correlations between party agendas and the mediated party agendas in the online news. These correlations represent the agenda selectivity each party experiences in each media outlet. The higher the correlation, the more congruent both agendas are.

Again, to measure the bias and not just outlet specificities, for each outlet the mean agenda selectivity of all other parties was subtracted from each party’s specific agenda selectivity value and then standardized to range from −1 to 1, where −1 stands for both agendas being not congruent at all and +1 stands for both agendas being identical.

This underlines the importance of studying all three types of media biases simultaneously, as examining just one aspect provides a misleading picture of the extent and nature of bias. The existence of various and diverse forms of media bias also means that it is worth considering their distinct effects on party preferences.